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Materials recovery facilities (MRFs) require new automated technologies if growing recycling demands are to be met. Current optical screening devices use visible (VIS) and near-infrared (NIR) wavelengths, frequency ranges that can experience challenges during the characterization of postconsumer plastic waste (PCPW) because of the overly-absorbing spectral bands from dyes and other polymer additives. Technological bottlenecks such as these contribute to 91% of plastic waste never actually being recycled. The mid-infrared (MIR) region has attracted recent attention due to inherent advantages over the VIS and NIR. The fundamental vibrational modes found therein make MIR frequencies promising for high fidelity machine learning (ML) classification. To-date, there are no ML evaluations of extensive MIR spectral datasets reflecting PCPW that would be encountered at MRFs. This study establishes quantifiable metrics, such as model accuracy and prediction time, for classification of a comprehensive MIR database consisting of five PCPW classes that are of economic interest: polyethylene terephthalate (PET #1), high-density polyethylene (HDPE #2), low-density polyethylene (LDPE #4), polypropylene (PP #5), and polystyrene (PS #6). Autoencoders, an unsupervised ML algorithm, were applied to the random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models. The RF model achieved accuracies of 100.0% in both the C–H stretching region (2990–2820 cm −1 ) and molecular fingerprint region (1500–650 cm −1 ). The C–H stretching region was found to be free from additives that were responsible for misclassification in other regions, making it a fruitful frequency range for future PCPW sorting technologies. The MIR classification of black plastics and polyethylene PCPW using ML autoencoders was also evaluated for the first time. 

Standoff detection based on optical spectroscopy is an attractive method for identifying materials at a distance with very high molecular selectivity. Standoff spectroscopy can be exploited in demanding practical applications such as sorting plastics for recycling. Here, we demonstrate selective and sensitive standoff detection of polymer films using bi-material cantilever-based photothermal spectroscopy. We demonstrate that the selectivity of the technique is sufficient to discriminate various polymers. We also demonstrate in situ, point detection of thin layers of polymers deposited on bi-material cantilevers using photothermal spectroscopy. Comparison of the standoff spectra with those obtained by point detection, FTIR, and FTIR-ATR show relative broadening of peaks. Exposure of polymers to UV radiation (365 nm) reveal that the spectral peaks do not change with exposure time, but results in peak broadening with an overall increase in the background cantilever response. The sensitivity of the technique can be further improved by optimizing the thermal sensitivity of the bi-material cantilever and by increasing the number of photons impinging on the cantilever.